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Coffee Leaf Rust (CLR) is a fungal epidemic disease that has been affecting coffee trees around the world since the 1980s. The early diagnosis of CLR would contribute strategically to minimize the impact on the crops and, therefore, protect the farmers’ profitability. In this research, a cyber-physical data-collection system was developed, by integrating Remote Sensing and Wireless Sensor Networks, to gather data, during the development of the CLR, on a test bench coffee-crop. The system is capable of automatically collecting, structuring, and locally and remotely storing reliable multi-type data from different field sensors, Red-Green-Blue (RGB) and multi-spectral cameras (RE and RGN). In addition, a data-visualization dashboard was implemented to monitor the data-collection routines in real-time. The operation of the data collection system allowed to create a three-month size dataset that can be used to train CLR diagnosis machine learning models. This result validates that the designed system can collect, store, and transfer reliable data of a test bench coffee-crop towards CLR diagnosis.

Laser beam shaping has been promising for many applications, such as two-photon polymerization[1], advanced microscopy[2], laser material processing[3]. Typical components are apertures, filters, diffractive optical elements (DOEs), refractive lenses, etc. So far, most designs have been focusing on how to shape a laser beam in a co-axial optical path, whereas certain applications require tilt geometry for a skew optical path [4]. Beam shaping problem is in general an inversed problem, which can be solved by ray-mapping, supporting quadric and Monge-Ampère equation method [5]. Ray-mapping method has its superiority for its high compatibility for various applications. Our previous work has led to a generalized ray-mapping method (A paper under review), which solves the Monge-Ampère equations for versatile input and output irradiance distributions[6]. This method is quite practical and readily to use for optical designers. In this work, we have further investigated its application for a top-hat beam shaping in a slanted illumination configuration.

Materials play a key role in tissue engineering for the construction of 3-dimensional scaffolds that support the formation of a new extracellular matrix. They should be biocompatible, and for the fabrication of functional scaffolds, they should be mechanically robust, provide high resolution and printability factors to use in one-photon polymerization micro-stereolithography (OPP mu-SLA) technologies. Furthermore, applications where those materials are used, such as tissue regeneration or tissue substitutes, require fabrication approaches that allow the scalability of 3D scaffold for their clinical use. Therefore, both materials and technology need to be optimized and improved. We tackle two research tracks, one to provide high resolution and biocompatible materials that can be used in OPP- mu-SLA, and a second one to obtain a new mu-SLA configuration that can boost large-size scaffold fabrication for tissue engineering applications. In this work, we report our progress towards the formulation of a hydrogel based on the prototype resin X HYDRORES INX X100 from XPECT INX and we describe the configuration based on the beam shaping of a laser source to print centimeter scale scaffolds while conserving micro-scale features. We printed scaffolds with a commercial ABS resin and the bio-inert hydrogel from XPECT INX that allow us to compare resolution, printability, and mechanical stability. Our results evidence structures with voxel widths up to 20 mu m and lengths up to 23 mm by using uniform light sheets illumination patterns. This work set new alternatives for the material and the fabrication aspects of additive manufacturing for 3D biofabrication.

Freeform optics reveals many benefits for beam-shaping systems. We propose an approach by reproducible ray mapping functions and surface optimizations to significantly reduce the design complexity of freeform beam shaping optics.

Agricultural activity has always been threatened by the presence of pests and diseases that prevent the proper development of crops and negatively affect the economy of farmers. One of these pests is Coffee Leaf Rust (CLR), which is a fungal epidemic disease that affects coffee trees and causes massive defoliation. As an example, this disease has been affecting coffee trees in Colombia (the third largest producer of coffee worldwide) since the 1980s, leading to devastating losses between 70% and 80% of the harvest. Failure to detect pathogens at an early stage can result in infestations that cause massive destruction of plantations and significantly damage the commercial value of the products. The most common way to detect this disease is by walking through the crop and performing a human visual inspection. As a result of this problem, different research studies have proven that technological methods can help to identify these pathogens. Our contribution is an experiment that includes a CLR development stage diagnostic model in the Coffea arabica, Caturra variety, scale crop through the technological integration of remote sensing (through drone capable multispectral cameras), wireless sensor networks (multisensor approach), and Deep Learning (DL) techniques. Our diagnostic model achieved an F1-score of 0.775. The analysis of the results revealed a p-value of 0.231, which indicated that the difference between the disease diagnosis made employing a visual inspection and through the proposed technological integration was not statistically significant. The above shows that both methods were significantly similar to diagnose the disease.

Wearable monitoring devices are now a usual commodity in the market, especially for the monitoring of sports and physical activity. However, specialized wearable devices remain an open field for high-risk professionals, such as military personnel, fire and rescue, law enforcement, etc. In this work, a prototype wearable instrument, based on reconfigurable technologies and capable of monitoring electrocardiogram, oxygen saturation, and motion, is presented. This reconfigurable device allows a wide range of applications in conjunction with mobile devices. As a proof-of-concept, the reconfigurable instrument was been integrated into ad hoc glasses, in order to illustrate the non-invasive monitoring of the user. The performance of the presented prototype was validated against a commercial pulse oximeter, while several alternatives for QRS-complex detection were tested. For this type of scenario, clustering-based classification was found to be a very robust option.